Open AccessIdentification and Stochastic Optimizing the UAV Motion Control in Turbulent Atmosphere
Author(s) -
Yevgeny Somov,
Nikolay Rodnishchev,
Tatyana Somova
Publication year - 2021
Publication title -
international journal of aviation science and technology
Language(s) - English
Resource type - Journals
ISSN - 2687-525X
DOI - 10.23890/ijast.vm02is02.0202
Subject(s) - control theory (sociology) , autopilot , identification (biology) , nonlinear system , moment (physics) , computer science , identifiability , multiplicative function , turbulence , system identification , multiplicative noise , mathematical optimization , mathematics , engineering , control engineering , control (management) , transmission (telecommunications) , physics , meteorology , artificial intelligence , mathematical analysis , machine learning , classical mechanics , biology , quantum mechanics , botany , signal transfer function , database , analog signal , telecommunications , measure (data warehouse)
In a class of diffusion Markov processes, we formulate a problem of identification of nonlinear stochastic dynamic systems with random parameters, multiplicative and additive noises, control functions, and the state vector at a final time moment. For such systems, the identifiability conditions are being studied, and necessary conditions are formulated in terms of the general theory of extreme problems. The developed engineering methods for identification and optimizing nonlinear stochastic systems are presented as well as their application for unmanned aerial vehicles under wind disturbances caused by atmospheric turbulence, namely, for optimizing the autopilot parameters during a rotary maneuver of an unmanned aerial vehicle in translational motion, taking into account the identification of its angular velocities.